{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialization\n",
    "\n",
    "Welcome to the first assignment of \"Improving Deep Neural Networks\". \n",
    "\n",
    "Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help learning.  \n",
    "\n",
    "If you completed the previous course of this specialization, you probably followed our instructions for weight initialization, and it has worked out so far. But how do you choose the initialization for a new neural network? In this notebook, you will see how different initializations lead to different results. \n",
    "\n",
    "A well chosen initialization can:\n",
    "- Speed up the convergence of gradient descent\n",
    "- Increase the odds of gradient descent converging to a lower training (and generalization) error \n",
    "\n",
    "To get started, run the following cell to load the packages and the planar dataset you will try to classify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation\n",
    "from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# load image dataset: blue/red dots in circles\n",
    "train_X, train_Y, test_X, test_Y = load_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You would like a classifier to separate the blue dots from the red dots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Neural Network model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will use a 3-layer neural network (already implemented for you). Here are the initialization methods you will experiment with:  \n",
    "- *Zeros initialization* --  setting `initialization = \"zeros\"` in the input argument.\n",
    "- *Random initialization* -- setting `initialization = \"random\"` in the input argument. This initializes the weights to large random values.  \n",
    "- *He initialization* -- setting `initialization = \"he\"` in the input argument. This initializes the weights to random values scaled according to a paper by He et al., 2015. \n",
    "\n",
    "**Instructions**: Please quickly read over the code below, and run it. In the next part you will implement the three initialization methods that this `model()` calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X, Y, learning_rate = 0.01, num_iterations = 15000, print_cost = True, initialization = \"he\"):\n",
    "    \"\"\"\n",
    "    Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input data, of shape (2, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 for red dots; 1 for blue dots), of shape (1, number of examples)\n",
    "    learning_rate -- learning rate for gradient descent \n",
    "    num_iterations -- number of iterations to run gradient descent\n",
    "    print_cost -- if True, print the cost every 1000 iterations\n",
    "    initialization -- flag to choose which initialization to use (\"zeros\",\"random\" or \"he\")\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model\n",
    "    \"\"\"\n",
    "        \n",
    "    grads = {}\n",
    "    costs = [] # to keep track of the loss\n",
    "    m = X.shape[1] # number of examples\n",
    "    layers_dims = [X.shape[0], 10, 5, 1]\n",
    "    \n",
    "    # Initialize parameters dictionary.\n",
    "    if initialization == \"zeros\":\n",
    "        parameters = initialize_parameters_zeros(layers_dims)\n",
    "    elif initialization == \"random\":\n",
    "        parameters = initialize_parameters_random(layers_dims)\n",
    "    elif initialization == \"he\":\n",
    "        parameters = initialize_parameters_he(layers_dims)\n",
    "\n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "\n",
    "        # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n",
    "        a3, cache = forward_propagation(X, parameters)\n",
    "        \n",
    "        # Loss\n",
    "        cost = compute_loss(a3, Y)\n",
    "\n",
    "        # Backward propagation.\n",
    "        grads = backward_propagation(X, Y, cache)\n",
    "        \n",
    "        # Update parameters.\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\n",
    "        \n",
    "        # Print the loss every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print(\"Cost after iteration {}: {}\".format(i, cost))\n",
    "            costs.append(cost)\n",
    "            \n",
    "    # plot the loss\n",
    "    plt.plot(costs)\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('iterations (per hundreds)')\n",
    "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "    plt.show()\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Zero initialization\n",
    "\n",
    "There are two types of parameters to initialize in a neural network:\n",
    "- the weight matrices $(W^{[1]}, W^{[2]}, W^{[3]}, ..., W^{[L-1]}, W^{[L]})$\n",
    "- the bias vectors $(b^{[1]}, b^{[2]}, b^{[3]}, ..., b^{[L-1]}, b^{[L]})$\n",
    "\n",
    "**Exercise**: Implement the following function to initialize all parameters to zeros. You'll see later that this does not work well since it fails to \"break symmetry\", but lets try it anyway and see what happens. Use np.zeros((..,..)) with the correct shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_zeros \n",
    "\n",
    "def initialize_parameters_zeros(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    parameters = {}\n",
    "    L = len(layers_dims)            # number of layers in the network\n",
    "    \n",
    "    for l in range(1, L):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.zeros((layers_dims[l],layers_dims[l-1]))\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
    "        ### END CODE HERE ###\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "b1 = [[0.]\n",
      " [0.]]\n",
      "W2 = [[0. 0.]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_zeros([3,2,1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.  0.  0.]\n",
    " [ 0.  0.  0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.  0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using zeros initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6931471805599453\n",
      "Cost after iteration 1000: 0.6931471805599453\n",
      "Cost after iteration 2000: 0.6931471805599453\n",
      "Cost after iteration 3000: 0.6931471805599453\n",
      "Cost after iteration 4000: 0.6931471805599453\n",
      "Cost after iteration 5000: 0.6931471805599453\n",
      "Cost after iteration 6000: 0.6931471805599453\n",
      "Cost after iteration 7000: 0.6931471805599453\n",
      "Cost after iteration 8000: 0.6931471805599453\n",
      "Cost after iteration 9000: 0.6931471805599453\n",
      "Cost after iteration 10000: 0.6931471805599455\n",
      "Cost after iteration 11000: 0.6931471805599453\n",
      "Cost after iteration 12000: 0.6931471805599453\n",
      "Cost after iteration 13000: 0.6931471805599453\n",
      "Cost after iteration 14000: 0.6931471805599453\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.5\n",
      "On the test set:\n",
      "Accuracy: 0.5\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"zeros\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance is really bad, and the cost does not really decrease, and the algorithm performs no better than random guessing. Why? Lets look at the details of the predictions and the decision boundary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions_train = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0]]\n",
      "predictions_test = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print (\"predictions_train = \" + str(predictions_train))\n",
    "print (\"predictions_test = \" + str(predictions_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with Zeros initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is predicting 0 for every example. \n",
    "\n",
    "In general, initializing all the weights to zero results in the network failing to break symmetry. This means that every neuron in each layer will learn the same thing, and you might as well be training a neural network with $n^{[l]}=1$ for every layer, and the network is no more powerful than a linear classifier such as logistic regression. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- The weights $W^{[l]}$ should be initialized randomly to break symmetry. \n",
    "- It is however okay to initialize the biases $b^{[l]}$ to zeros. Symmetry is still broken so long as $W^{[l]}$ is initialized randomly. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Random initialization\n",
    "\n",
    "To break symmetry, lets intialize the weights randomly. Following random initialization, each neuron can then proceed to learn a different function of its inputs. In this exercise, you will see what happens if the weights are intialized randomly, but to very large values. \n",
    "\n",
    "**Exercise**: Implement the following function to initialize your weights to large random values (scaled by \\*10) and your biases to zeros. Use `np.random.randn(..,..) * 10` for weights and `np.zeros((.., ..))` for biases. We are using a fixed `np.random.seed(..)` to make sure your \"random\" weights  match ours, so don't worry if running several times your code gives you always the same initial values for the parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_random\n",
    "\n",
    "def initialize_parameters_random(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)               # This seed makes sure your \"random\" numbers will be the as ours\n",
    "    parameters = {}\n",
    "    L = len(layers_dims)            # integer representing the number of layers\n",
    "    \n",
    "    for l in range(1, L):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l],layers_dims[l-1]) * 10\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 17.88628473   4.36509851   0.96497468]\n",
      " [-18.63492703  -2.77388203  -3.54758979]]\n",
      "b1 = [[0.]\n",
      " [0.]]\n",
      "W2 = [[-0.82741481 -6.27000677]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_random([3, 2, 1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 17.88628473   4.36509851   0.96497468]\n",
    " [-18.63492703  -2.77388203  -3.54758979]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[-0.82741481 -6.27000677]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using random initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ChengzhiYang\\深度学习作业\\第二课第一周编程作业\\assignment1\\init_utils.py:145: RuntimeWarning: divide by zero encountered in log\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n",
      "C:\\Users\\ChengzhiYang\\深度学习作业\\第二课第一周编程作业\\assignment1\\init_utils.py:145: RuntimeWarning: invalid value encountered in multiply\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: inf\n",
      "Cost after iteration 1000: 0.6250982793959966\n",
      "Cost after iteration 2000: 0.5981216596703697\n",
      "Cost after iteration 3000: 0.5638417572298645\n",
      "Cost after iteration 4000: 0.5501703049199763\n",
      "Cost after iteration 5000: 0.5444632909664456\n",
      "Cost after iteration 6000: 0.5374513807000807\n",
      "Cost after iteration 7000: 0.4764042074074983\n",
      "Cost after iteration 8000: 0.39781492295092263\n",
      "Cost after iteration 9000: 0.3934764028765484\n",
      "Cost after iteration 10000: 0.3920295461882659\n",
      "Cost after iteration 11000: 0.38924598135108\n",
      "Cost after iteration 12000: 0.3861547485712325\n",
      "Cost after iteration 13000: 0.384984728909703\n",
      "Cost after iteration 14000: 0.3827828308349524\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.83\n",
      "On the test set:\n",
      "Accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"random\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you see \"inf\" as the cost after the iteration 0, this is because of numerical roundoff; a more numerically sophisticated implementation would fix this. But this isn't worth worrying about for our purposes. \n",
    "\n",
    "Anyway, it looks like you have broken symmetry, and this gives better results. than before. The model is no longer outputting all 0s. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 1 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 1\n",
      "  1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0\n",
      "  0 0 0 0 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0\n",
      "  1 0 1 1 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0\n",
      "  0 0 1 0 1 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1\n",
      "  1 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 1\n",
      "  0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 1 1 0 1 1\n",
      "  1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1\n",
      "  1 1 1 1 0 0 0 1 1 1 1 0]]\n",
      "[[1 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 1\n",
      "  0 1 1 0 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0\n",
      "  1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print (predictions_train)\n",
    "print (predictions_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with large random initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "- The cost starts very high. This is because with large random-valued weights, the last activation (sigmoid) outputs results that are very close to 0 or 1 for some examples, and when it gets that example wrong it incurs a very high loss for that example. Indeed, when $\\log(a^{[3]}) = \\log(0)$, the loss goes to infinity.\n",
    "- Poor initialization can lead to vanishing/exploding gradients, which also slows down the optimization algorithm. \n",
    "- If you train this network longer you will see better results, but initializing with overly large random numbers slows down the optimization.\n",
    "\n",
    "<font color='blue'>\n",
    "**In summary**:\n",
    "- Initializing weights to very large random values does not work well. \n",
    "- Hopefully intializing with small random values does better. The important question is: how small should be these random values be? Lets find out in the next part! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - He initialization\n",
    "\n",
    "Finally, try \"He Initialization\"; this is named for the first author of He et al., 2015. (If you have heard of \"Xavier initialization\", this is similar except Xavier initialization uses a scaling factor for the weights $W^{[l]}$ of `sqrt(1./layers_dims[l-1])` where He initialization would use `sqrt(2./layers_dims[l-1])`.)\n",
    "\n",
    "**Exercise**: Implement the following function to initialize your parameters with He initialization.\n",
    "\n",
    "**Hint**: This function is similar to the previous `initialize_parameters_random(...)`. The only difference is that instead of multiplying `np.random.randn(..,..)` by 10, you will multiply it by $\\sqrt{\\frac{2}{\\text{dimension of the previous layer}}}$, which is what He initialization recommends for layers with a ReLU activation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_he\n",
    "\n",
    "def initialize_parameters_he(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    parameters = {}\n",
    "    L = len(layers_dims)  # integer representing the number of layers\n",
    "     \n",
    "    for l in range(1, L):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l],layers_dims[l-1]) *  np.sqrt(2./layers_dims[l-1])\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 1.78862847  0.43650985]\n",
      " [ 0.09649747 -1.8634927 ]\n",
      " [-0.2773882  -0.35475898]\n",
      " [-0.08274148 -0.62700068]]\n",
      "b1 = [[0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "W2 = [[-0.03098412 -0.33744411 -0.92904268  0.62552248]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_he([2, 4, 1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 1.78862847  0.43650985]\n",
    " [ 0.09649747 -1.8634927 ]\n",
    " [-0.2773882  -0.35475898]\n",
    " [-0.08274148 -0.62700068]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[-0.03098412 -0.33744411 -0.92904268  0.62552248]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using He initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.8830537463419761\n",
      "Cost after iteration 1000: 0.6879825919728063\n",
      "Cost after iteration 2000: 0.6751286264523371\n",
      "Cost after iteration 3000: 0.6526117768893807\n",
      "Cost after iteration 4000: 0.6082958970572937\n",
      "Cost after iteration 5000: 0.5304944491717495\n",
      "Cost after iteration 6000: 0.4138645817071793\n",
      "Cost after iteration 7000: 0.3117803464844441\n",
      "Cost after iteration 8000: 0.23696215330322556\n",
      "Cost after iteration 9000: 0.18597287209206828\n",
      "Cost after iteration 10000: 0.15015556280371808\n",
      "Cost after iteration 11000: 0.12325079292273548\n",
      "Cost after iteration 12000: 0.09917746546525937\n",
      "Cost after iteration 13000: 0.08457055954024274\n",
      "Cost after iteration 14000: 0.07357895962677366\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.9933333333333333\n",
      "On the test set:\n",
      "Accuracy: 0.96\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"he\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with He initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "- The model with He initialization separates the blue and the red dots very well in a small number of iterations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 5 - Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "You have seen three different types of initializations. For the same number of iterations and same hyperparameters the comparison is:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "        <td>\n",
    "        **Model**\n",
    "        </td>\n",
    "        <td>\n",
    "        **Train accuracy**\n",
    "        </td>\n",
    "        <td>\n",
    "        **Problem/Comment**\n",
    "        </td>\n",
    "\n",
    "    </tr>\n",
    "        <td>\n",
    "        3-layer NN with zeros initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        50%\n",
    "        </td>\n",
    "        <td>\n",
    "        fails to break symmetry\n",
    "        </td>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with large random initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        83%\n",
    "        </td>\n",
    "        <td>\n",
    "        too large weights \n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with He initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        99%\n",
    "        </td>\n",
    "        <td>\n",
    "        recommended method\n",
    "        </td>\n",
    "    </tr>\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember from this notebook**:\n",
    "- Different initializations lead to different results\n",
    "- Random initialization is used to break symmetry and make sure different hidden units can learn different things\n",
    "- Don't intialize to values that are too large\n",
    "- He initialization works well for networks with ReLU activations. "
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "deep-neural-network",
   "graded_item_id": "XOESP",
   "launcher_item_id": "8IhFN"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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